NEWS RELEASE
EGEE GRID ATTACKS AVIAN FLU
During April, a collaboration of Asian and European laboratories has
analysed 300,000 possible drug components against the avian flu virus
H5N1 using the EGEE Grid infrastructure. The goal was to find potential
compounds that can inhibit the activities of an enzyme on the surface of
the influenza virus, the so-called neuraminidase, subtype N1. Using the
Grid to identify the most promising leads for biological tests could speed
up the development process for drugs against the influenza virus.
One of the targets of existing drugs today on the market is viral
neuraminidase, an enzyme that helps the virus to proliferate and infect
more cells. As this protein is known to evolve into variants if it comes
under drug stress, drug resistance becomes a potential concern in case
of an influenza pandemic.
The challenge of the in silico drug discovery application is to identify
those molecules which can dock on the active sites of the virus in order
to inhibit its action. To study the impact of small scale mutations on drug
resistance, a large set of compounds was screened against the same
neuraminidase target but with various, slightly different structures. With
the results from the in silico screening, researchers can predict which
compounds and chemical fragments are most effective for blocking the
active neuraminidases in case of mutations.
The drug discovery process is being greatly accelerated by the use of
the EGEE and associated computing Grid infrastructures. For the
docking of 300,000 compounds against 8 different target structures of
Influenza A neuraminidases, 2000 computers were used during 4 weeks
in April – the equivalent of 100 years on a single computer. More than 60
000 output files with a data volume of 600 Gigabytes were created and
stored in a relational database. Potential drug compounds against avian
flu are now being identified and ranked according to the binding energies
of the docked models.
“With the help of the high-speed computing and huge data managing
capabilities of the Grid, possible drug components can be screened and
studied very rapidly by the available computer modelling applications,”
says Ying-Ta Wu, biologist at the Genomics Research Center of the
Academia Sinica in Taipei. “This will free up medicinal chemists’ time to
better respond to instant, large-scale threats. Moreover, we can
concentrate our biological assays in the laboratory on the most
promising components, the ones we expect to have the greatest impact.”
“With these results, the Grid demonstrates that it is a powerful and
reliable resource for scientists, opening up new research possibilities
and improving existing methods,” said Viviane Reding, European
Commissioner responsible for Information Society and Media. “I am very
grateful to see that the European flagship Grid infrastructure is
EGEE-II is a project funded by the European Commission - contract number INFSO-RI-031688
NEWS RELEASE
contributing to solving current and socially important problems such as
avian flu.”
Taking advantage of the experience acquired in the previous WISDOM
data challenge on malaria, the Grid-enabled in silico process was
implemented in less than a month on three different grid infrastructures:
AuverGrid, EGEE, and TWGrid, paving the way for a virtual drug
screening service at a large scale. The majority of computing is
conducted on the WISDOM platform; in addition, a light-weight
application framework called DIANE was also adopted in this challenge
and used to perform a sizeable fraction of the total activity to enable
efficient computing resource integration and usage. The next WISDOM
data challenge against several targets of neglected diseases will take
place in autumn 2006.
This drug discovery application against the avian flu virus was jointly
deployed by the Genomics Research Center, Academia Sinica, Taiwan;
Academia Sinica Grid Computing Team, Taiwan; Corpuscular Physics
Laboratory of Clermont-Ferrand, CNRS/IN2P3, France; Institute for
Biomedical Technologies, CNR, Italy, in collaboration with the EGEE
project, the AuverGrid regional grid in Auvergne, and the TWGrid. This
work took place in collaboration with the EMBRACE network of
excellence and the BioInfoGrid project.
Note to Editors:
1. To speed-up and reduce the cost to develop new drugs, researchers use in silico
docking algorithms to compute the probability that potential drugs will dock with a target
protein. In silico drug screening can thus accelerate the discovery of novel potent
inhibitors by minimising the non-productive trial-and-error approach in a laboratory.
2. For more information on the drug discovery application against the avian flu virus,
please contact Ying-Ta Wu (GRC, Academia Sinica), email: ywu@gate.sinica.edu.tw
3. For more information about WISDOM (wide in silico docking against malaria),
please visit: http://wisdom.eu-egee.fr/ or contact Nicolas Jacq (CNRS/IN2P3), email:
jacq@clermont.in2p3.fr.
4. For more information about the Enabling Grids for E-sciencE (EGEE) project
please see http://www.eu-egee.org/ or contact Hannelore Hammerle (CERN), EGEE
External Relations Officer, telephone: +41 22 767 4176 or email:
hannelore.hammerle@cern.ch
5. For more information about DIANE (Distributed Analysis Environment), please
visit: http://cern.ch/diane/
6. For more information on the Corpuscular Physics Laboratory of Clermont-Ferrand,
CNRS/IN2P3 please see http://clrwww.in2p3.fr/
7. For more information on the Genomics Research Center of Academia Sinica,
Taiwan, please see http://www.genomics.sinica.edu.tw/
8. For more information on the Academia Sinica Grid Computing Team (ASGC),
Taiwan, please see http://www.twgrid.org/
9. For more information on the Institute for Biomedical Technologies, CNR, Italy,
please see http://www.itb.cnr.it/
10. For more information on the AuverGrid please see http://www.auvergrid.fr/
11. For more information on EMBRACE, please see http://www.embracegrid.info
12. For more information on the Bioinformatics Grid Application for life science
(BioinfoGRID) project, please see http://www.itb.cnr.it/bioinfogrid
EGEE-II is a project funded by the European Commission - contract number INFSO-RI-031688